This study aimed to design a web-based application for computer-aided diagnosis (CADx) of intraosseous jaw lesions, and assess its diagnostic accuracy. In this diagnostic test study, a web-based application was designed for CADx of 19 types of intraosseous jaw lesions. To assess its diagnostic accuracy, clinical and radiographic information of 95 cases with confirmed histopathological diagnosis of intraosseous jaw lesions were retrieved from hospital archives and published literature and imported to the application by a senior dental student. The top-N accuracy, kappa value, and Brier score were calculated, and the sensitivity, specificity, positive (PPV) and negative (NPV) predictive values, and the area under the receiver operating characteristic (ROC) curve (AUC) were calculated separately for each lesion according to DeLong et al. In assessment of top-N accuracy, the designed application gave a correct differential diagnosis in 93 cases (97.89%); the correct diagnosis was at the top of the list of differential diagnoses in 78 cases (82.10%); these values were 85 (89.47%) and 67 (70.52%) for an oral radiologist. The kappa value was 0.53. The Brayer score for the prevalence match was 0.18, and the pattern match was 0.15. The results highlighted the optimally high diagnostic accuracy of the designed application, indicating that it may be reliably used for CADx of intraosseous jaw lesions, if given accurate data.

Artificial intelligence (AI) represents a transformative opportunity for pain medicine, offering potential solutions to longstanding challenges in pain assessment and management. This review synthesizes the current state of AI applications with a strategic framework for implementation, highlighting established adaptation pathways from adjacent medical fields. In acute pain, AI systems have achieved regulatory approval for ultrasound guidance in regional anesthesia and shown promise in automated pain scoring through facial expression analysis. For chronic pain management, machine learning algorithms have improved diagnostic accuracy for musculoskeletal conditions and enhanced treatment selection through predictive modeling. Successful integration requires interdisciplinary collaboration and physician coleadership throughout the development process, with specific adaptations needed for pain-specific challenges. This roadmap outlines a comprehensive methodological framework for AI in pain medicine, emphasizing four key phases: problem definition, algorithm development, validation, and implementation. Critical areas for future development include perioperative pain trajectory prediction, real-time procedural guidance, and personalized treatment optimization. Success ultimately depends on maintaining strong partnerships between clinicians, developers, and researchers while addressing ethical, regulatory, and educational considerations.

The aim of the study was to compare the diagnostic quality of deep learning (DL) reconstructed balanced steady-state free precession (bSSFP) single-shot (SSH) cine images with standard, multishot (also: segmented) bSSFP cine (standard cine) in cardiac MRI. This prospective study was performed in a cohort of participants with clinical indication for cardiac MRI. SSH compressed-sensing bSSFP cine and standard multishot cine were acquired with breath-holding and electrocardiogram-gating in short-axis view at 1.5 Tesla. SSH cine images were reconstructed using an industry-developed DL super-resolution algorithm (DL-SSH cine). Two readers evaluated diagnostic quality (endocardial edge definition, blood pool to myocardium contrast and artifact burden) from 1 (nondiagnostic) to 5 (excellent). Functional left ventricular (LV) parameters were assessed in both sequences. Edge rise distance, apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio were calculated. Statistical analysis for the comparison of DL-SSH cine and standard cine included the Student's t-test, Wilcoxon signed-rank test, Bland-Altman analysis, and Pearson correlation. Forty-five participants (mean age: 50 years ±18; 30 men) were included. Mean total scan time was 65% lower for DL-SSH cine compared to standard cine (92 ± 8 s vs 265 ± 33 s; P  < 0.0001). DL-SSH cine showed high ratings for subjective image quality (eg, contrast: 5 [interquartile range {IQR}, 5-5] vs 5 [IQR, 5-5], P  = 0.01; artifacts: 4.5 [IQR, 4-5] vs 5 [IQR, 4-5], P  = 0.26), with superior values for sharpness parameters (endocardial edge definition: 5 [IQR, 5-5] vs 5 [IQR, 4-5], P  < 0.0001; edge rise distance: 1.9 [IQR, 1.8-2.3] vs 2.5 [IQR, 2.3-2.6], P  < 0.0001) compared to standard cine. No significant differences were found in the comparison of objective metrics between DL-SSH and standard cine (eg, aSNR: 49 [IQR, 38.5-70] vs 52 [IQR, 38-66.5], P  = 0.74). Strong correlation was found between DL-SSH cine and standard cine for the assessment of functional LV parameters (eg, ejection fraction: r = 0.95). Subgroup analysis of participants with arrhythmia or unreliable breath-holding (n = 14/45, 31%) showed better image quality ratings for DL-SSH cine compared to standard cine (eg, artifacts: 4 [IQR, 4-5] vs 4 [IQR, 3-5], P  = 0.04). DL reconstruction of SSH cine sequence in cardiac MRI enabled accelerated acquisition times and noninferior diagnostic quality compared to standard cine imaging, with even superior diagnostic quality in participants with arrhythmia or unreliable breath-holding.

Respiratory motion poses a significant challenge for clinical workflows in diagnostic imaging and radiation therapy. Many technologies such as motion artifact reduction and tumor tracking have been developed to compensate for its effect. To assess these technologies, respiratory motion phantoms (RMPs) are required as preclinical testing environments, for instance, in computed tomography (CT). However, current CT RMPs are highly simplified and do not exhibit realistic tissue structures or deformation patterns. With the rise of more complex motion compensation technologies such as deep learning-based algorithms, there is a need for more realistic RMPs. This work introduces PixelPrint 4D , a 3D printing method for fabricating lifelike, patient-specific deformable lung phantoms for CT imaging. A 4DCT dataset of a lung cancer patient was acquired. The volumetric image data of the right lung at end inhalation was converted into 3D printer instructions using the previously developed PixelPrint software. A flexible 3D printing material was used to replicate variable densities voxel-by-voxel within the phantom. The accuracy of the phantom was assessed by acquiring CT scans of the phantom at rest, and under various levels of compression. These phantom images were then compiled into a pseudo-4DCT dataset and compared to the reference patient 4DCT images. Metrics used to assess the phantom structural accuracy included mean attenuation errors, 2-sample 2-sided Kolmogorov-Smirnov (KS) test on histograms, and structural similarity index (SSIM). The phantom deformation properties were assessed by calculating displacement errors of the tumor and throughout the full lung volume, attenuation change errors, and Jacobian errors, as well as the relationship between Jacobian and attenuation changes. The phantom closely replicated patient lung structures, textures, and attenuation profiles. SSIM was measured as 0.93 between the patient and phantom lung, suggesting a high level of structural accuracy. Furthermore, it exhibited realistic nonrigid deformation patterns. The mean tumor motion errors in the phantom were ≤0.7 ± 0.6 mm in each orthogonal direction. Finally, the relationship between attenuation and local volume changes in the phantom had a strong correlation with that of the patient, with analysis of covariance yielding P  = 0.83 and f  = 0.04, suggesting no significant difference between the phantom and patient. PixelPrint 4D facilitates the creation of highly realistic RMPs, exceeding the capabilities of existing models to provide enhanced testing environments for a wide range of emerging CT technologies.

This retrospective study aims to develop a machine learning model integrating preoperative CT radiomics and clinicopathological data to predict 3-year recurrence and recurrence patterns in postoperative oesophageal squamous cell carcinoma. Tumour regions were segmented using 3D-Slicer, and radiomic features were extracted via Python. LASSO regression selected prognostic features for model integration. Clinicopathological data include tumour length, lymph node positivity, differentiation grade, and neurovascular infiltration. Ultimately, a machine learning model was established by combining the screened imaging feature data and clinicopathological data and validating model performance. A nomogram was constructed for survival prediction, and risk stratification was carried out through the prediction results of the machine learning model and the nomogram. Survival analysis was performed for stage-based patient subgroups across risk stratifications to identify adjuvant therapy-benefiting cohorts. Patients were randomly divided into a 7:3 ratio of 368 patients in the training cohorts and 158 patients in the validation cohorts. The LASSO regression screens out 6 recurrence prediction and 9 recurrence pattern prediction features, respectively. Among 526 patients (mean age 63; 427 males), the model achieved high accuracy in predicting recurrence (training cohort AUC: 0.826 [logistic regression]/0.820 [SVM]; validation cohort: 0.830/0.825) and recurrence patterns (training:0.801/0.799; validation:0.806/0.798). Risk stratification based on a machine learning model and nomogram predictions revealed that adjuvant therapy significantly improved disease-free survival in stages II-III patients with predicted recurrence and low survival (HR 0.372, 95% CI: 0.206-0.669; p < 0.001). Machine learning models exhibit excellent performance in predicting recurrence after surgery for squamous oesophageal cancer. Radiomic features of contrast-enhanced CT imaging can predict the prognosis of patients with oesophageal squamous cell carcinoma, which in turn can help clinicians stratify risk and screen out patient populations that could benefit from adjuvant therapy, thereby aiding medical decision-making. There is a lack of prognostic models for oesophageal squamous cell carcinoma in current research. The prognostic prediction model that we have developed has high accuracy by combining radiomics features and clinicopathologic data. This model aids in risk stratification of patients and aids clinical decision-making through predictive outcomes.

Femoroacetabular impingement (FAI) with cam-type morphology is a common hip disorder that can result in groin pain and eventually osteoarthritis. The pre-operative assessment is based on parameters obtained from x-ray or computed tomography (CT) scans, namely alpha angle (AA) and femoral head-neck offset (FHNO). The goal of our study was to develop a computer-aided detection (CAD) system to automatically select the hip region and measure diagnostic parameters from CT scans to overcome the limitations of the tedious and time-consuming process of subjectively selecting CT image slices to obtain parameters. 271 cases of ordinary abdominopelvic CT examination were collected retrospectively from two hospitals between 2018 and 2022, each equipped with a distinct CT scanner. First, a convolution neural network (CNN) was designed to select hip region slices among abdominopelvic CT scan image series. This CNN was trained using 80 CT scans divided into 50%, 20%, and 30% for training, validation and testing groups, respectively. Second, the most appropriate oblique slice passing through the femoral head-neck complex was selected, and AA and FHNO landmarks were calculated using image-processing algorithms. The best oblique slices were selected/measured manually for each hip as ground truth and its related parameters. CT hip-region selection using CNN yielded 99.34% accuracy. Pearson correlation coefficient between manual and automatic parameters measurement were 0.964 and 0.856 for AA and FHNO, respectively. The results of this study are promising for future development of a CAD software application for screening CT scans that may aid physicians to assess FAI. Question Femoroacetabular impingement is a common, underdiagnosed hip disorder requiring time-consuming image-based measurements. Can AI improve the efficiency and consistency of its radiologic assessment? Findings Automated slice selection and landmark detection using a hybrid AI method improved measurement efficiency and accuracy, with minimal bias confirmed through Bland-Altman analysis. Clinical relevance An AI-based method enables faster, more consistent evaluation of cam-type femoroacetabular impingement in routine CT images, supporting earlier identification and reducing dependency on operator experience in clinical workflows.

Brain metastases (BMs) are the most common intracranial malignancy, often arising from lung, breast, and melanoma cancers. Receptor tyrosine kinases, such as EGFR and HER2, drive tumor progression and resistance to therapy. Noninvasive detection of these biomarkers, especially in brain metastases, is crucial due to challenges with traditional biopsy methods. This systematic review and meta-analysis assess machine learning (ML)-based models for detecting EGFR mutations and HER2 overexpression in metastatic brain adenocarcinoma using MRI-derived radiomic features. A systematic review and meta-analysis were conducted following PRISMA 2020 guidelines. Studies were identified via PubMed, Scopus, and Web of Science, focusing on ML applications to MRI radiomics for detecting EGFR and HER2 in brain metastases. Data on study design, imaging modality, model type, sample size, and performance metrics were extracted. Subgroup analyses were performed by model type (deep learning vs. classical ML) and sample size (<150 vs. ≥150 participants). A random-effects model was used to pool performance metrics, and risk of bias was assessed using the RoB 2 tool. STATA version 18 and Python 3.10 were used for analyses and visualizations. Of 383 identified studies, 31 (7925 participants) met the inclusion criteria. The pooled analysis showed strong diagnostic performance: AUC = 0.84, accuracy = 0.86, and sensitivity = 0.83. Subgroup analysis revealed higher AUC and accuracy in deep learning models compared with classical ML. Sensitivity analysis also indicated improved AUC in studies with larger sample sizes (≥150), though variability remained. No evidence of heterogeneity or publication bias was detected. ML models demonstrate strong diagnostic performance for detecting EGFR and HER2 in metastatic brain adenocarcinoma, supporting their potential as noninvasive diagnostic tools. However, these findings should be interpreted considering methodological heterogeneity and the limited use of external validation. Further prospective, multicenter studies are warranted to confirm their clinical applicability and generalizability.

The clinical significance of medullary abnormalities in the appendicular skeleton detected by computed tomography (CT) in patients with multiple myeloma (MM) remains incompletely elucidated. This study aims to validate novel low-dose CT-based methods for quantifying myeloma bone marrow (BM) volume in the appendicular skeleton of MM patients undergoing [¹⁸F]FDG PET/CT. Seventy-two newly diagnosed, transplantation eligible MM patients enrolled in the randomised phase 3 GMMG-HD7 trial underwent whole-body [¹⁸F]FDG PET/CT prior to treatment and after induction therapy with either isatuximab plus lenalidomide, bortezomib, and dexamethasone or lenalidomide, bortezomib, and dexamethasone alone. Two CT-based methods using the Medical Imaging Toolkit (MITK 2.4.0.0, Heidelberg, Germany) were used to quantify BM infiltration in the appendicular skeleton: (1) Manual approach, based on calculation of the highest mean CT value (CTv) within bony canals. (2) Semi-automated approach, based on summation of CT values across the appendicular skeleton to compute cumulative CT values (cCTv). PET/CT data were analyzed visually and via standardized uptake value (SUV) metrics, applying the Italian Myeloma criteria for PET Use (IMPeTUs). Additionally, an AI-based method was used to automatically derive whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) from PET scans. Post-induction, all patients were evaluated for minimal residual disease (MRD) using BM multiparametric flow cytometry. Correlation analyses were performed between imaging data and clinical, histopathological, and cytogenetic parameters, as well as treatment response. Statistical significance was defined as p < 0.05. At baseline, the median CTv (manual) was 26.1 Hounsfield units (HU) and the median cCTv (semi-automated) was 5.5 HU. Both CT-based methods showed weak but significant correlations with disease burden indicators: CTv correlated with BM plasma cell infiltration (r = 0.29; p = 0.02) and β2-microglobulin levels (r = 0.28; p = 0.02), while cCTv correlated with BM plasma cell infiltration (r = 0.25; p = 0.04). Appendicular CT values further demonstrated significant associations with PET-derived parameters. Notably, SUVmax values from the BM of long bones were strongly correlated with both CTv (r = 0.61; p < 0.001) and moderately with cCTv (r = 0.45; p < 0.001). Patients classified as having increased [¹⁸F]FDG uptake in the BM (Deauville Score ≥ 4), according to the IMPeTUs criteria, exhibited significantly higher CTv and cCTv values compared to those with Deauville Score <4 (p = 0.002 for both). AI-based analysis of PET data revealed additional weak-to-moderate significant associations, with MTV correlating with CTv (r = 0.32; p = 0.008) and cCTv (r = 0.45; p < 0.001), and TLG showing correlations with CTv (r = 0.36; p = 0.002) and cCTv (r = 0.46; p < 0.001). Following induction therapy, CT values decreased significantly from baseline (median CTv = -13.8 HU, median cCTv = 5.2 HU; p < 0.001 for both), and CTv significantly correlated with SUVmax values from the BM of long bones (r = 0.59; p < 0.001). In parallel, the incidence of follow-up pathological PET/CT scans, SUV values, Deauville Scores, and AI-derived MTV and TLG values showed a significant reduction after therapy (all p < 0.001). No significant differences in CTv, cCTv, or PET-derived metrics were observed between MRD-positive and MRD-negative patients. Novel CT-based quantification approaches for assessing BM involvement in the appendicular skeleton correlate with key clinical and PET parameters in MM. As low-dose, standardized techniques, they show promise for inclusion in MM imaging protocols, potentially enhancing assessment of disease extent and treatment response.

<b>Background:</b> Insights into the nature of false-positive findings flagged by contemporary mammography artificial intelligence (AI) systems could inform the potential use of AI to reduce false-positive recall rates. <b>Objective:</b> To compare AI and radiologists in terms of characteristics of false-positive digital breast tomosynthesis (DBT) examinations in a breast cancer screening population. <b>Methods:</b> This retrospective study included 2977 women (mean age, 58 years) participating in an observational population-based screening study who underwent 3183 screening DBT examinations from January 2013 to June 2017. A commercial AI tool analyzed DBT examinations. Positive examinations were defined for AI as an elevated-risk result and for interpreting radiologists as BI-RAD category 0. False-positive examinations were defined as the absence of a breast cancer diagnosis within 1 year. Radiologists re-reviewed the imaging for AI-flagged false-positive findings. <b>Results:</b> The false-positive rate was 10% for both AI (308/3183) and radiologists (304/3183). Of 541 total false-positive examinations, 233 (43%) were false positives for AI only, 237 (44%) for radiologists only, and 71 (13%) for both. AI-only versus radiologist-only false positives were associated with greater mean patient age (60 vs 52 years, p<.001), lower frequency of dense breasts (24% vs 57%, p<.001), and greater frequencies of a personal history of breast cancer (13% vs 4%, p<.001), prior breast imaging studies (95% vs 78%, p<.001), and prior breast surgical procedures (37% vs 11%, p<.001). The false-positive examinations included 932 AI-only flagged findings, 315 radiologist-only flagged findings, and 49 flagged findings concordant between AI and radiologists. AI-only flagged findings were most commonly benign calcifications (40%), asymmetries (13%), and benign postsurgical change (12%); radiologist-only flagged findings were most commonly masses (47%), asymmetries (19%), and indeterminate calcifications (15%). Of 18 concordant flagged findings undergoing biopsy, 44% yielded high-risk lesions. <b>Conclusion:</b> Imaging and patient-level differences were observed between AI and radiologist false-positive DBT examinations. Although only a small fraction of false-positive examinations overlapped between AI and radiologists, concordant flagged findings had a high rate of representing high-risk lesions. <b>Clinical Impact:</b> The findings may help guide strategies for using AI to improve DBT recall specificity. In particular, concordant findings may represent an enriched subset of actionable abnormalities.

Advances in fetal three-dimensional (3D) ultrasound (US) and magnetic resonance imaging (MRI) have revolutionized the study of fetal brain development, enabling detailed analysis of brain structures and growth. Despite their complementary capabilities, these modalities capture fundamentally different physical signals, potentially leading to systematic differences in image-derived phenotypes (IDPs). Here, we evaluate the agreement of IDPs between US and MRI by comparing the volumes of eight brain structures from 90 subjects derived using deep-learning algorithms from majority same-day imaging (days between scans: mean = 1.2, mode = 0 and max = 4). Excellent agreement (intra-class correlation coefficient, <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mi>ICC</mi> <mo>></mo> <mn>0.75</mn></mrow> <annotation>$$ ICC>0.75 $$</annotation></semantics> </math> ) was observed for the cerebellum, cavum septum pellucidum, thalamus, white matter and deep grey matter volumes, with significant correlations <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <mfenced><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </mfenced> </mrow> <annotation>$$ \left(p<0.001\right) $$</annotation></semantics> </math> for most structures, except the ventricular system. Bland-Altman analysis revealed some systematic biases: intracranial and cortical plate volumes were larger on US than MRI, by an average of <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mn>35</mn> <mspace></mspace> <msup><mi>cm</mi> <mn>3</mn></msup> </mrow> <annotation>$$ 35\ {\mathrm{cm}}^3 $$</annotation></semantics> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mn>4.1</mn> <mspace></mspace> <msup><mi>cm</mi> <mn>3</mn></msup> </mrow> <annotation>$$ 4.1\ {\mathrm{cm}}^3 $$</annotation></semantics> </math> , respectively. Finally, we found the labels of the brainstem and ventricular system were not comparable between the modalities. These findings highlight the necessity of structure-specific adjustments when interpreting fetal brain IPDs across modalities and underscore the complementary roles of US and MRI in advancing fetal neuroimaging.